1. Developing Country Studies www.iiste.org
ISSN 2224-607X (Paper) ISSN 2225-0565 (Online)
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A Comparative Analysis of Fertility Differentials in Cross River
State
Ushie, M. A.,Otu, Judith.E.,Undelikwo, V. A.
Department of Sociology, University of Calabar, Nigeria
Email: jasame03@yahoo.com
Abstract
This paper seek to evaluate fertility differentials in a rural –urban residents in Cross River State. Two
settlements were use which include Anantigha as an urban settlement in Calabar and Bendi as a rural settlement
in Obanliku. Ninety households were used for this study of which equal number of questionnaires was randomly
distributed in each of the settlement. Findings show that there was no variation in age entry to marriage in the
two settlements even though there was a significant difference in fertility differentials in the study area Besides,
it was noticed in the study area that family size and composition in the study area does not have any influence
on fertility differences.
Keywords: Comparative, Differentials, Fertility, Marriage , Residents.
Introduction
Today, not every factor implicated in fertility is important and directly affects fertility. Some factor are direct
while others apply through the direct variables (Bongaarts, 1978). Those that exact themselves directly on
fertility, Bongaarts refers to as the proximate determinants while the indirect ones are the socio-economic and
other background variables. Proximate determinants of fertility are behavioral and biological factors. It is the
knowledge of the proximate determinants that improves the understanding of operation of the socio-economic
variables. What Bongaarts refers to as “proximate” determinants had been earlier termed “intermediate”
determinants by Davis and Blake (1956). By intermediate he meant that these variables stand between socio-
economic conditions and fertility. The influence of socio-economic conditions can only be felt through the
intermediate variables. According to UN(1987:165), whatever reduces or increases fertility level , takes through
“the direct operation of various factors affecting the exposure to intercourse and exposure to conception and
through factors affecting pregnancy outcomes and length of the post partum infecundable period” and these
variables extend to more remote influences such as educational and cultural background. Many multivariate
studies have been conducted to engage the casual factors linked with fertility. However, these studies have
proved inadequate and in many cases, the key problematic is the issue of methodology, that is, of data collection.
Most researchers depend on official statistics which for obvious political and other reasons may be unreliable.
Thus, findings from such studies do reflect the data, which are usually unreliable. It is in the light of the obvious
gaps in the available knowledge and the intractable nature of the problem that this study is designed to fill the
said lacunae with respect to investigating the influence of education, family size and marital union on fertility
differences in Cross River State.
Literature review
Proximate determinants of fertility
According to Bongaarts (1978) not every factor implicated in fertility is important and directly affects
fertility. Some factors are direct while others apply through the direct variables. Those that exact themselves
directly on fertility, Bongaarts refers to as the proximate determinants while the indirect ones are the
socioeconomic and other background variables. Proximate determinants of fertility are behavioural and
biological factors. It is the knowledge of the proximate determinants that improves the understanding of
operation of the socioeconomic variables. What Bongaarts refers to as “proximate” determinants that had been
earlier termed “intermediate” determinants by Davis and Blake (1956). By intermediate is meant that these
variables stand between socioeconomic conditions and fertility. The influence of socioeconomic conditions can
only be felt through the intermediate variables. According to UN(1987:165) whatever reduces or increases
fertility level takes placed through “the direct operation of various factors affecting the exposure to intercourse
and exposure to conception, and through factors affecting pregnancy outcomes and length of the post partum
infecundable period”. And these variables extend to more remote influences such as education and cultural
background. Therefore, factors accountable for variation in fertility can be accounted for by these proximate
determinants. This implies that differentials and trends of fertility within a country and differences in fertility
levels across countries can be directly traced to differences in these proximate variables if it can be assumed
that the potential level of fertility is the same in all societies and all factors directly affecting fertility have been
fully accounted for. In sum, there are therefore, three factors that determine fertility trends and differentials.
- Factors affecting exposure to intercourse

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- Factors affecting exposure to conception and
- Factors affecting gestation and successful parturition
A major convolution of Bongaarts (1978) to the understanding of fertility is the development of a
model in which three main proximate determinants of fertility could be measured and their relative effects on
fertility qualified. In doing this, Bongaarts restricted the factors to be considered to the four most important
variables:
1. Marriage (which is only one aspect of exposure to sexual intercourse
2. Contraception (or exposure to risk of contraception
3. Abortion (one aspect of gestational outcome and
4. Breastfeeding (the most important determinant of the duration of infecundity following a birth.
Other proximate or intermediate variables such as primary or secondary sterility or infecundity, temporary
separation between married couples and other reasons for involuntary abstinence were not considered by
Bongaarts because he felt that their fertility impact would not vary greatly across population. Studies have
confirmed that most of fertility variation in the majority of countries can be explained by these four factors
alone (Bongaarts, 1978; 1982; Bongaarts and Kirmeyer, 1982; Isiugo-Abanihe, 1996). The model developed by
Bongaarts expresses the actual level of fertility, (the total fertility rate, TRF) as a function of the fertility-
reducing effects of the proximate determinants on a maximum potential level of fertility (the total fecundity rate
TF). The equation or the model is summarized as:-
TFR=Cm.Cc.Ca.Ci.TF
Where Cm represents the index of marriage, Cc is the index of contraception, Ca is the index of
abortion and Ci is the index of postpartum infecundity. The implication is that is any society or group of people
where the fertility-reducing effects of the proximate determinants is lower, the outcome will be a higher total
fertility rate. Several studies have omitted the index of abortion (Ca) from the model especially in Africa
claiming that its effect on fertility in Africa is negligible. This may well be contested, but one must bear in
mind that societal laws also affect the smooth operation of the determinants of fertility (Isiugo-Abanihe, 1996),
and since out society frowns at abortion, this may well be left out. So utilizing the proximate determinants of
fertility model shown above, Isiugo-Abanihe (1996) studied the determinant of fertility in Nigeria. It will be
very pertinent to reviee Isiugo-Abanihe, is (1996) work here while at the same time pointing to the factors
determining fertility deferential. In examining marriage as a proximate determinant, he divided the issues into
age at first marriage, non-marriage or celibacy, marital disruption and remarriage. He noted in 1990 that the
median age at first marital unions was 17 in Nigeria. This means that half of Nigeria women aged 15-19 have
married by the time they are 17years old.
On his own part, Lightbourne (2007) analysis revealed that there was a positive association between
size of place of residence and the proportion of women currently practicing contraception. The association held
for all age groups and for all parity levels. The proportion of women at risk and currently practicing
contraception was 55% in principal cities, 47% in other urban areas, and 33% in rural areas. His findings also
indicated that contraception was widely practiced for the spacing purposes. The percent of women ever using a
method. For every 100 ever users there were 70.5 current users in urban areas and 61.3 current users in rural
areas. Rural and urban differences in contraceptive use for the 19 countries were compared with rural and urban
differences in industrialized countries. The rate of current urban users/100 rural users in industrial countries
was 107. Respective rates for the Asian and Pacific region and the Latin American region were 152 and 155.
Findings were presented in a series of 33 tables. The mean age at first marriage in 1990 was 17.3 while the
singulate mean age at marriage (an estimate of the mean age at first marriage of those who ever marry) was
about 20 years. Note that these generalized statements do conceal significant variations in marriage behaviours
among the component parts of the country. For example, “age at a first marriage is higher in urban areas than in
rural areas, and among educated women relative to those with little schooling: (Isiugo-Abahihe, 1996:11).
Moreover, there is a substantial ethnic variation in age at marriage in Nigeria, with a pattern of very early
marriage among the Hausa/Fulani (mean age at first marriage less that 15 years), and fairly late marriage
among the Yoruba and the Igbo (mean age at first marriage higher than 19 years). Today, many works have been
conducted in this area but none has been able x-ray the subject matter with specific reference to the study area
Methodology
The study was conducted in Cross River State taking into consideration, two environments rural and urban. The
rural community used for this research include Bench Community of Obanliku Local Government Area. The two
area covered in the urban environment was state housing estate in Calabar municipality and Anantigha in
Calabar South Local Government Area. Bench represented a rural community while Calabar was used because is
the most urbanized place in Cross River State. The population sample was used which 900 household consist of
married men and women. The sample was derived by using 10% of the total households in each of the selected
areas. This means that, from bench 350 households were selected from 3,500 households whose Anantigha had

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420 from 4,200 households; this brings the total to 900. The ten (10) villages of Bendi was used of which each
produced 35 respondents while in the urban area (state housing and Anantigha) were randomly selected. The
questionnaire were designed to elicit data from the respondents which were used for the comparative analysis
of fertility differences between rural/urban environment
Finding
Rural-urban fertility differentials
The rural-urban fertility differentials presented in table 1 show that, the calculated x2
– value of 90.91 is higher
than the critical X2
-value of 9.49 at .05 level of significance with 4 degree of freedom. With this result, the null
hypothesis that, difference in age at entry to marital unions between rural and urban residents is not
significantly related to rural/urban fertility differentials was rejected. This implies that, difference in age at
entry into marital unions between rural and urban residential is significantly related to rural /urban fertility
difference.
Table 1: Rural-urban fertility differentials
Age Rural Densely urban Low densely urban Total X2
value
Below 20 101(77.27) 79(93.18) 20(29.55) 200
20-30 208(198.20) 255(239.01) 50(75.78) 513 90.91
30 and above 31(64.52) 76(77.81) 60(24.67) 167
Total 340 410 130 880
Source: Data analysis , 2012.
The age of entry into marital union and rural – density
The rural-urban fertility differentials presented urban presented in table 2 shows the calculated x2
-
value of 20.2 is higher than the critical X2
value of 1.386 at .05 level of significance with 2 degree of freedom.
With this result, the null hypothesis that said differences in age at entry into marital unions between rural and
urban residents is not significantly related was rejected. This however, shows a comparison between rural and
densely urban area (Anantigha). It implies that differences in age at entry into marital union between the areas is
significantly related and do exist. The result of this hypothesis revealed that differences in age of entry into
marital unions between rural and urban residents are significantly related to rural/urban fertility differentials.
The findings are line with the view of Bhatia (1978) who observed that age at marriage whether proximate or
intermediate determinant of fertility has direct bearing or effect on fertility. The determination of when to start
family or age of marriage was a function of socioeconomic variables such as educational demands, chosen
career, suitable suitors, and economic backgrounds, among others. Leon (2004) noted that in Nigeria the law
states that a girl must at least complete her basic education and must be at least 18 years before entering into
marriage unions. Enforcing such as law in Nigeria is not easy especially given the cultural diversity of the
country.
Table 2: Age of entry into marital union and rural/densely urban
Age Rural Densely urban Total X2
value
Below 20 101(81.6) 79(98.4) 180
20-30 209(209.89) 355(253.11) 463
30 and above 31(48.51) 76(58.49) 107 20.02*
Total 340 410 750
Source: Data analysis , 2012.
The result in table 3 indicates that, the calculated X2
-value of 23.83 is higher than the critical X2
-value
of 5.99 at .05 level of significance with 2 degree of freedom. With this result the null hypothesis that said
“differences in the choice of family size and composition between rural and urban residents does not have any
significant influence on fertility differences” was rejected. This means that, the choice of family size and
composition between rural and urban residents have a significant influence on their fertility differences as
maintained in the alternate hypothesis.
Table 3:Result of statistical analysis of the influence of family size and composition in rural/urban fertility
Family size and composition Rural/urban fertility differences
Rural Densely urban Low densely urban Total X2
value
Small 90(100.45) 150 (121.14) 20(38.41) 260
Large 250 (239.55) 260(288.86) 110(91.59) 620 23.83*
Total 340 410 130 880
Source: Data analysis , 2012
The result in table 4 shows the calculated X2
-value of 6.45 is higher than the critical value X2
of 0.455
at .05 level significance with 1 degree of freedom. With this result, the null hypothesis on the influence of
family size and composition in rural/urban areas was rejected, and the alternate hypothesis is upheld, hence it

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establishes a high degree of influence between rural and low density areas differently.
Table 4:Result of statistical analysis of the influence of family size on densely urban fertility
Family size Rural Densely urban Total X2
value
Small 90(100.45) 150 (131.2) 240
Large 250 (231.2) 260(278.8) 510 8.74*
Total 340 410 750
Source: Data analysis , 2012
Table 5: Result of statistical analysis of the influence of family size and low densely urban fertility
Family size Rural Low densely urban Total X2
value
Small 90(75.57) 20(30.43) 110
Large 250 (260.43) 110(99.57) 360 6.45*
Total 340 130 470
Source: Data analysis , 2012
Recommendations
It has been observed that fertility rate in the study area this is evidenced in the data collected however, the
increasing rate of fertility in the study area was attributed to age of entering to married and socioeconomic
variables. This study has shown that even though there was variation in fertility differentials in both rural and
urban environment, the following measures are recommended to avert increasing rate of fertility in the study area.
Families should adopt family planning measures so as to reduce fertility rate
The age for entering into marriage should be specify so as to prevent early marriages
There should be public enlightenment on the dangers associated to high fertility rate in any given
environment
The government should establishment a department that would be charge with the responsibility of
monitoring the rate of fertility in both rural and urban areas
Conclusion
Today, fertility differentials in both rural and urban areas can be attributed to many factors. This study has
shown that even though the rate of fertility in urban area is high compared to rural area, the rate of fertility in
both settlements seem to be very high due to age of entering to marriage and socioeconomic attributes of the
residents in the study area. Besides, it was observed that family size and composition influence fertility
differentials in study area. Therefore, adequate measures must be put in place by the government and other
agencies to check the increasing rate of fertility in the area.
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